Early detection of impending physiologic deterioration among patients who are not in intensive care: development of predictive models using data from an automated electronic medical record.
about
Translational bioinformatics in the era of real-time biomedical, health care and wellness data streamsEHDViz: clinical dashboard development using open-source technologiesUsing electronic health record data to develop and validate a prediction model for adverse outcomes in the wards*.Big data analytics to improve cardiovascular care: promise and challenges.Opportunities and challenges in developing risk prediction models with electronic health records data: a systematic review.Data that drive: Closing the loop in the learning hospital system.Fluid volume, lactate values, and mortality in sepsis patients with intermediate lactate valuesPARAMO: a PARAllel predictive MOdeling platform for healthcare analytic research using electronic health records.Development and validation of an electronic medical record-based alert score for detection of inpatient deterioration outside the ICU.Piloting electronic medical record-based early detection of inpatient deterioration in community hospitals.Multicenter development and validation of a risk stratification tool for ward patients.Hospital readmission and healthcare utilization following sepsis in community settings.Automated identification of pneumonia in chest radiograph reports in critically ill patientsMeasuring the modified early warning score and the Rothman index: advantages of utilizing the electronic medical record in an early warning systemA randomized trial of real-time automated clinical deterioration alerts sent to a rapid response team.Why the C-statistic is not informative to evaluate early warning scores and what metrics to use.Nonelective Rehospitalizations and Postdischarge Mortality: Predictive Models Suitable for Use in Real Time.Variables associated with unplanned general adult ICU admission in hospitalised patients: protocol for a systematic reviewMulticenter Comparison of Machine Learning Methods and Conventional Regression for Predicting Clinical Deterioration on the WardsKeeping Score of Severity Scores: Taking the Next Step.The value of vital sign trends for detecting clinical deterioration on the wards.Developing new predictive alarms based on ECG metrics for bradyasystolic cardiac arrestPlacing clinical variables on a common linear scale of empirically based risk as a step towards construction of a general patient acuity score from the electronic health record: a modelling studyRisk stratification of hospitalized patients on the wards.The Natural History of Changes in Preferences for Life-Sustaining Treatments and Implications for Inpatient Mortality in Younger and Older Hospitalized AdultsMulticenter Implementation of a Treatment Bundle for Patients with Sepsis and Intermediate Lactate Values.Real-Time Automated Sampling of Electronic Medical Records Predicts Hospital Mortality.Towards prevention of acute syndromes: electronic identification of at-risk patients during hospital admission.Determining criteria to assess patient readiness for discharge from postanaesthetic care: an international Delphi study.Participatory design of probability-based decision support tools for in-hospital nurses.A review of recent advances in data analytics for post-operative patient deterioration detection.Identifying Distinct Subgroups of ICU Patients: A Machine Learning Approach.Randomized Trial of Intelligent Sensor System for Early Illness Alerts in Senior Housing.Is everyone really breathing 20 times a minute? Assessing epidemiology and variation in recorded respiratory rate in hospitalised adults.Enhanced Recovery After Surgery Program Implementation in 2 Surgical Populations in an Integrated Health Care Delivery System.Incorporating an Early Detection System Into Routine Clinical Practice in Two Community Hospitals.The Timing of Early Antibiotics and Hospital Mortality in Sepsis.Implementation of a novel postoperative monitoring system using automated Modified Early Warning Scores (MEWS) incorporating end-tidal capnography.Validation and Comparison of Seven Mortality Prediction Models for Hospitalized Patients With Acute Decompensated Heart FailureAutomated detection of physiologic deterioration in hospitalized patients.
P2860
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P2860
Early detection of impending physiologic deterioration among patients who are not in intensive care: development of predictive models using data from an automated electronic medical record.
description
2012 nî lūn-bûn
@nan
2012 թուականի Մարտին հրատարակուած գիտական յօդուած
@hyw
2012 թվականի մարտին հրատարակված գիտական հոդված
@hy
2012年の論文
@ja
2012年論文
@yue
2012年論文
@zh-hant
2012年論文
@zh-hk
2012年論文
@zh-mo
2012年論文
@zh-tw
2012年论文
@wuu
name
Early detection of impending p ...... ted electronic medical record.
@ast
Early detection of impending p ...... ted electronic medical record.
@en
Early detection of impending p ...... ted electronic medical record.
@nl
type
label
Early detection of impending p ...... ted electronic medical record.
@ast
Early detection of impending p ...... ted electronic medical record.
@en
Early detection of impending p ...... ted electronic medical record.
@nl
prefLabel
Early detection of impending p ...... ted electronic medical record.
@ast
Early detection of impending p ...... ted electronic medical record.
@en
Early detection of impending p ...... ted electronic medical record.
@nl
P2093
P356
P1476
Early detection of impending p ...... ted electronic medical record.
@en
P2093
Arona Ragins
Benjamin J Turk
David Draper
Gabriel J Escobar
Juan Carlos LaGuardia
Patricia Kipnis
P304
P356
10.1002/JHM.1929
P577
2012-03-22T00:00:00Z